Biology subjects

Ianeselli, A.

Publications and source records attributed to Ianeselli, A..

4 recordsLinked to original sources

bioRxiv2025

Generative World Models to compute protein folding pathways

We developed a generative World Model in the context of biomolecular simulations for proteins. An agent was trained by evolution strategy in a latent spatio-temporal representation of the structural transitions, to learn a policy to drive protein folding simulations via dihedral rotations ({Phi}, {Psi}). Latent configurations were decoded back into atomistic structures using atomistic force fields, allowing for the calculation of structural properties and energetic terms. We show how the model can be regularized by adding Ramachandran-based rewards during the training of the controller. Results have been validated against equilibrium MD data. Markov State Modeling could be applied to reconstruct the dynamics of the system and reconstruct the unbiased folding landscape. The method proposed here facilitates the study of the conformational changes and folding pathways of proteins by generative AI, and can have application in structure-based drug discovery.

biophysics↗

bioRxiv2024

A Discard-and-Restart MD algorithm for the sampling of realistic protein transition states and enhance structure-based drug discovery

We introduce a Discard-and-Restart molecular dynamics (MD) algorithm tailored for the sampling of realistic protein transition states. It aids computational structure-based drug discovery by reducing the simulation times to compute transition pathways by up to 2000x. The algorithm iteratively performs short MD simulations and measures their proximity to a target state via a collective variable (CV) loss, which can be defined in a flexible fashion, locally or globally. Using the loss, if the trajectory proceeds toward the target, the MD simulation continues. Otherwise, it is discarded and a new MD simulation is restarted, with new initial velocities randomly drawn from a Boltzmann distribution. The discard-and-restart algorithm demonstrates efficacy and atomistic accuracy in capturing the folding pathways in several contexts: (1) fast-folding small protein domains; (2) the folding intermediate of the prion protein PrP; and (3) the spontaneous partial unfolding of -Tubulin, a crucial event for microtubule severing. During each iteration of the algorithm, we are able to perform AI-based analysis of the transitory conformations to find binding pockets, which could potentially represent druggable sites. Overall, our algorithm enables systematic and computationally efficient exploration of conformational landscapes, enhancing the design of ligands targeting dynamic protein states.

biophysics↗

bioRxiv2021

Non-equilibrium conditions inside rock pores drive fission, maintenance and selection of coacervate protocells

Key requirements for the first cells on Earth include the ability to compartmentalize and evolve. Compartmentalization spatially localizes biomolecules from a dilute pool and an evolving cell which grows and divides permits mixing and propagation of information to daughter cells. Complex coacervate microdroplets are excellent candidates as primordial cells with the ability to partition and concentrate molecules into their core and support primitive and complex biochemical reactions. However, the evolution of coacervate protocells by fusion, growth and fission has not yet been demonstrated. In this work, a primordial environment initiated the evolution of coacervate-based protocells. Gas bubbles inside heated rock pores perturb the coacervate protocell distribution and drive the growth, fusion, division and selection of coacervate microdroplets. This setting provides a primordial non-equilibrium environment. Our findings describe how common gas bubbles within heated rock pores induce the early evolution processes of coacervate-based protocells, providing a compelling scenario for the evolution of membrane-free coacervate microdroplets on the early Earth.

biophysics↗

bioRxiv2020

PHARMACOLOGICAL PROTEIN INACTIVATION BY TARGETING FOLDING INTERMEDIATES

Recent computational advancements in the simulation of biochemical processes allow investigating the mechanisms involved in protein regulation with realistic physics-based models, at an atomistic level of resolution. Using these techniques to study the negative regulation of the androgen receptor (AR), we discovered a key functional role played by non-native metastable states appearing along the folding pathway of this protein. This unexpected observation inspired us to design a completely novel drug discovery approach, named Pharmacological Protein Inactivation by Folding Intermediate Targeting (PPI-FIT), based on the rationale of negatively regulating protein expression by targeting folding intermediates. Here, PPI-FIT was tested for the first time on the cellular prion protein (PrP), a cell surface glycoprotein playing a key role in fatal and transmissible neurodegenerative pathologies known as prion diseases. We predicted the all-atom structure of an intermediate appearing along the folding pathway of PrP, and identified four different small molecule ligands for this conformer, all capable of selectively lowering the expression of the protein by promoting its degradation. Our data support the notion that the level of target proteins could be modulated by acting on their folding pathways, implying a previously unappreciated role for folding intermediates in the biological regulation of protein expression.

biochemistry↗